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Balancing for AA Content with NRC 2001and the Formulate2 Dairy Ration Optimizer Central Valley Nutritional Associates, LLC Copyright 2009 by CVNA, LLC.

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Presentation on theme: "Balancing for AA Content with NRC 2001and the Formulate2 Dairy Ration Optimizer Central Valley Nutritional Associates, LLC Copyright 2009 by CVNA, LLC."— Presentation transcript:

1 Balancing for AA Content with NRC 2001and the Formulate2 Dairy Ration Optimizer Central Valley Nutritional Associates, LLC Copyright 2009 by CVNA, LLC – All Rights Reserved

2 Introduction During the conference season Dr. Brian Sloan of Adisseo presented a series of seminars on balancing for AA. The subject of these seminars was a hypothetical “Western Diet” that was evaluated with CPM and then reformulated for AA content also using CPM. This presentation reviews both Dr. Sloan’s basal and reformulated diets for the purpose of comparing the supply predictions of these specific diets when evaluated with CPM, CNCPS 6.1(beta version) and NRC 2001/Formulate2. The purpose of this review is to highlight both the similarities and the significant differences between model predictive mechanisms in the context of a specific diet. While useful and interesting, generalized reviews comparing models may have little relevance in such a context. Additionally, despite many years of competent and focused research as well as successful field experience, there appears to be a lingering perception that balancing for AA “doesn’t work”. It is hoped that what is presented here will help provide answers as to the reasons that perception persists as well as offer information that will permit more wide-spread success formulating for AA. We would like to express our appreciation to Dr. Sloan for providing us with detailed diet information from his CPM session files. Central Valley Nutritional Associates, LLC

3 Stipulations The NRC data used in this presentation was developed using the Formulate2 Dairy Ration Optimizer which implements the NRC 2001 model. The.nrc file included on the jump drive for use with the NRC evaluation software was generated with that software after the Formulate2 data. DM and CP were verified but no attempt was made to verify the A, B and C protein fractions and Kd values with those used in Formulate2. Consequently, you will find small differences between some of the NRC data in the presentation and the corresponding data in the accompanying.nrc file. However, as you will see, those minor differences are inconsequential with respect to the model comparisons made here. Also, the CNCPS 6.1 file will exhibit similar small differences. The original data was developed with the beta version immediately preceding the current beta version which expires in September of These small differences are a result of similarly small DM differences and the manner in which the software handles changes to DM%. But again, with respect to the substantial differences in values generated by predictive mechanisms, these minor input differences also have no significant impact. Data from the CPM session files that appears in the power point presentation were generated with NO changes to efficiency constants or rates. These are "out-of-the-box" CPM evaluations - they are what you get if you are NOT making adjustments to CPM constants and rates. The CNCPS 6.1 data are also from "out-of-the-box" evaluations which is the only option with CNCPS 6.1. since efficiency constants are NOT accessible by the user and NO ADJUSTMENTS CAN BE MADE. Central Valley Nutritional Associates, LLC

4 Principal Concepts 1.A conservative estimate of MP-AA from rumen microbial yield is critical to successfully balancing lactating diets for AA content. Without it you can kiss animal response good-bye! Over estimating MP-AA from microbial yield can significantly shift the partitioning of predicted supplies of MP-AA from feeds to microbial yield at formulation time. If the “optimistic” microbial yield doesn’t materialize in the rumen neither will the predicted MP-AA supplies. The greater the predicted microbial yield the greater the potential MP-AA supply short-fall and the greater the probability that animal response will not be realized. 2.A conservative estimate of MP-AA from feeds is just as critical to achieving desired animal response. Over estimating MP-AA from feeds has exactly the same effect on actual supplies delivered to the duodenum. Again, the greater the prediction of MP-AA from feeds the greater the probability that animal response will not be realized. Central Valley Nutritional Associates, LLC

5 Principal Concepts 1.A conservative estimate of MP-AA from rumen microbial yield is critical to successfully balancing lactating diets for AA content. Without it you can kiss animal response good-bye! 2.A conservative estimate of MP-AA from feeds is just as critical to achieving desired animal response. What follows is an illustration of these concepts in practical terms. The supplies of MP and MP-AA from rumen microbial yield and MP and MP-AA from feeds predicted by the mechanisms of CPM, CNCPS 6.1 and NRC 2001 within the context of a single diet are compared. This gives us a chance to see not only how these models predict supplies with respect to one another but also how differences in prediction can affect animal response by their effect on actual, delivered supplies. We invite you to review the material presented here and draw your own conclusions. Central Valley Nutritional Associates, LLC

6 Theoretical Models Attempt to express metabolic function in terms of predictive mechanisms Attempt to account for the interaction of inputs to metabolic processes Attempt to accurately describe resulting metabolic outputs Strive to accurately describe metabolic realities experienced by the animal

7 Acknowledging Model Differences CPM/CNCPS – theoretical and fully mechanistic – “real world” accuracy wholly dependent on theoretical mechanisms NRC 2001 – empirical/semi-mechanistic – theoretical mechanisms are constrained by measured data providing a “reality check” correction factor to enhance “real world” accuracy “In contrast to the described factorial models (CNCPS/Rulquin AA sub-model) in which both the structure and the parameters where determined on theoretical grounds, the multivariate regression or semi-factorial approach allows for some of the parameters to be determined by regression. This allows the model (i.e., equations) to adapt to the measured data and allows for at least partial correction of the mechanistically determined variables…Because of the potential for increased accuracy of prediction …the semi-mechanistic method was the method of choice by the sub-committee…” (Nutrient Requirements of Dairy Cattle: Seventh Revised Edition 2001 pg 75)

8 The Adisseo “Western Diet” A comparison of model evaluations of the Adisseo “Western Diet” highlights the differences between CPM, CNCPS and NRC predictive mechanisms (Dry matter differences are nominal and have no significant effect) Feed Item AF WtCPMDM% NRCDM% DMCP% Alfalfa hay Corn silage Flaked corn Canola Meal Soybean hulls WCS DDGS Animal Protein Vitamin/mineral Cane molasses Tallow Primary Inputs 1334 lb Holstein 2 nd lactation lbs of milk 3.50% butterfat 3.30% milk CP 54.1 lbs DMI 120 DIM 3.0 BCS

9 Reserves differences and all other differences illustrated in this presentation are calculated from actual DMI Model predicted DMI values are very different as are the predicted daily changes in body reserves. The magnitude of the difference in predicted reserves changes, which for both models is based on DMI of 54 lbs, is very significant given the fact that the two predicted values are on opposites sides of zero. These differences are indicative of other differences reviewed here which are also quite significant.

10 This spread in Energy allowable milk is enormous and accounts for the difference in predicted reserves changes. The two models present a very different picture of what an animal on this diet with DMI of 54 lbs producing 100 lbs of milk will experience with respect to changes in BCS.

11 MP supply predictions also show substantial differences. The most significant of these is the prediction of MP from rumen microbial crude protein (MCP) at 256g.

12 All of the differences highlighted here are significant and create two divergent pictures of what to expect from this diet at the given DMI with respect to changes in BCS and both Energy allowable milk and MP allowable milk. For the purposes of this presentation, the most significant of these is the difference in predicted MP from MCP (256g). Comparison of CPM and NRC 2001 Predictive Mechanisms Because it is in an area particularly sensitive to inconsistencies in diet implementation, a 256g difference in predicted MP from MCP yield is especially significant. The implication of this difference is far greater than the difference in MP allowable milk and represents a range of almost 12.0 lbs of milk production that may not be realized if ruminal microbial yield is less than optimal.

13 What are the Implications of Differences in Predicted MP from MCP? The difference in predicted MP from MCP yield shown above (256g) represents a range of possible variability in MP allowable milk that is significantly greater than that suggested by the 8.1 lb difference seen in the table. Most issues associated with diet implementation in commercial dairy herds impact rumen fermentation and therefore also impact MCP yield. Since MP required from the diet is primarily the difference between the total MP requirement and MP from rumen microbial sources, if the diet is formulated on the basis of model predictions, the model prediction of MP from MCP yield determines MP required from the diet and therefore dictates the protein sources utilized in diet formulation. If MP from actual MCP yield is significantly less than the predicted supply, total MP supply will fall short of the MP requirement and that short-fall will impact milk and milk component production as well as lean tissue mobilization in fresh and early lactation animals. As seen above, in this scenario, the CPM estimate of MP from MCP yield is more than 119% greater than the NRC value. Consequently, achieving the result predicted by CPM MP allowable milk is far more dependent on obtaining optimal rumen MCP yield than is the NRC prediction. Therefore, in commercial production facilities where diet implementation is far less controlled than research facilities, a purely theoretical prediction of MP from MCP yield predicated on achieving an optimal rumen environment is likely to produce results significantly short of model predicted response. CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84%

14 What is a Significant Deficit in Model Predicted MP from MCP? The Perspective from CPM Predictions From the table above we can calculate the number of grams required for one pound of milk using the CPM prediction of MP for milk production and CPM predicted MP allowable milk (2161 / 99.7 = 21.7 g). A short-fall in the predicted MP from MCP yield of 10% would reduce total MP supply by 158g (1584 *.90 = 158.4). The impact of this reduction in predicted MP supply in terms of MP allowable milk is significant at 7.3 lbs of milk (158.4 / 21.7 = 7.3). The total difference between the CPM and NRC predictions of MP from ruminal MCP yield of 256g is equivalent to 11.8 lbs of milk (256 / 21.7 = 11.8). This difference represents a range of possible negative variation in anticipated animal response to which the CPM prediction of MP allowable milk is subject, relative to the NRC prediction, due to differences between actual and predicted MP from MCP yield. The NRC Perspective It should also be noted that any variation in actual MP from MCP yield within the area of difference between the CPM and NRC predictions (+ 1 to 256g) would have a positive effect on anticipated response based on the NRC prediction of MP allowable milk. From the NRC perspective then, this same area of variation in MP from MCP yield (256g) is a positive rather than a negative variation and represents a range of 12.3 lbs of milk (1903 / 91.6 = 20.8 and 256 / 20.8 = 12.3) which closely mirrors the CPM value of CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84%

15 Model Predicted MP from MCP Response Variability - CPM Response Variability - CPM A prediction of MP from MCP yield 119% greater than the NRC predicted value introduces a significant degree of possible negative variability in terms of anticipated animal response. The degree of variability actually experienced is likely to be greatest in commercial production settings where diet implementation is often far less controlled than in research settings and therefore has a greater impact on actual MCP yield. A 10% reduction in predicted MP from MCP resulting from issues with diet implementation can have a significant impact on anticipated production of milk and milk components. While with fresh and early lactation animals it may be difficult to identify significant differences in milk and milk component yields due to the ability of dairy cows to mobilize AA from lean tissue, if predicted milk and milk components are present when actual MP from MCP yield is less than predicted, the animal can be assumed to be meeting the short-fall in her need for MP-AA for milk and milk components at the expense of her lean tissue reserves. However, when lean tissue mobilization has reached its limits, MP, and therefore MP-AA, can be expected to become the primary limiting factor with respect to production of milk and milk components with production declining to the level supported by actual MCP yield and dietary MP. It is reasonable to assume that this phenomenon can possibly impact persistency after peak milk as well as other issues associated with significant depletion of lean tissue reserves. CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84%

16 Model Predicted MP from MCP Response Variability - NRC Response Variability – NRC While the same factors that affect CPM response variability also apply to response to the NRC prediction scenario, it is apparent that relative to CPM, the semi-mechanistic approach employed by NRC introduces a substantial buffer to variability of MCP yield in commercial production settings. As mentioned earlier, with respect to the NRC prediction of MP from MCP, MP from actual MCP yield in the range of possible variation between the two model predictions (1 to 256g) will find production of milk and milk components exceeding the NRC predicted response. It should also be noted that MP from actual MCP yield below the NRC prediction will produce far less negative variation from the NRC predicted MP allowable milk than from the CPM prediction. If this scenario with the Adisseo “Western Diet” is a general representation of the predictive mechanisms of the two models, the implication is that diets formulated with the NRC prediction model can be expected to contain higher levels of dietary MP than diets formulated with the CPM model. This is due to a more conservative approach to predicting MP from MCP yield which inherently requires a greater MP supply from the diet. This increased dietary MP supply, acts as a compensating mechanism for diet implementation issues in commercial production settings that negatively impact ruminal MCP yield. CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84%

17 NRC Prediction of MCP Yield Formulate2 allows adjustment of the initial NRC prediction of MCP yield as a percent of the model predicted value. To help illustrate differences between CPM and NRC with respect to predictions of MP from MCP yield, a value equal to the difference in basal diet evaluations of MP from MCP yield has been entered as an adjustment to NRC predicted MCP yield. CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84%

18 NRC Prediction of MCP Yield CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84% However, NRC imposes limits to MCP yield predictions relative to RDP supply that in this scenario will limit prediction of MP from MCP to a maximum of 1479 g. As shown in the data to the right, NRC indicates that the RDP supply of this diet will limit MCP yield and has therefore revised the adjusted prediction downward indicating that additional RDP is required to support the yield adjustment.

19 Summary of Differences in Model Predictions Response Variability Potential (RVP) from differences in predicted and actual MP from MCP due to actual vs. predicted ruminal MCP yield lbs to12.3 lbs CPMNRCDiff%NRC MP from Rumen Microbial sources*1584g1328g256g % *MCP Microbial Crude Protein MP for milk production2161g1903g258g113.56% MP allowable milk99.7 lbs 91.6 lbs 8.1 lbs107.84% Daily change in body reserves (lbs/day).90 lbs-1.14 lbs2.06 lbs177.59% From what has been illustrated by this comparison of model predictions from the Adisseo “Western Diet”, it is obvious that in the key areas of prediction summarized in the table above, significant differences exist between the applicable CPM and NRC predictive mechanisms. It is also clear that the area of response variability potential (RVP) created by the substantial difference in prediction of MP from MCP yield (256g) is quite significant and is most likely to be realized in commercial production settings where diet implementation can be far less controlled than in research settings. It should be noted that both models are in close agreement as to the magnitude of response variability potential (upper RVP = 11.8 to 12.3 lbs of milk). It is also apparent that the semi-mechanistic approach employed by NRC in which theoretical predictive mechanisms are constrained by measured data produces a far more conservative estimate of MP from ruminal MCP yield than CPM which significantly reduces the RVP for NRC MP predictions in commercial production settings. Having reviewed these important differences between CPM and NRC predictive mechanisms, using the same “Western Diet”, let’s take a look a quick look at how the predictive mechanisms of CNCPS version 6.1 compare with NRC.

20 Comparison of CNCPS 6.1 and NRC 2001 Predictive Mechanisms Though the CNCPS prediction of MP from rumen microbial yield is within a few grams of the NRC prediction, MP predicted from feeds is more than 130% higher than the NRC prediction. The net result is that CNCPS predicted MP allowable milk is even higher than the CPM prediction. The spread between CPM and NRC is 8.1 lbs of milk but the spread between CNCPS and NRC is 18.3 lbs of milk – more than double the difference between CPM and NRC. Based on differences in MP predicted from MCP yield and MP from feeds the CNCPS Response Variability Potential (RVP) is 20.8 lbs of milk, more than 150% greater than the CPM RVP of 11.8 (2278 / = 20.7 and 431 / 20.7 = 20.8) Though not of the same magnitude as MP allowable milk, Energy allowable milk is also predicted higher than CPM. The red arrows highlight differences between the predictive mechanisms of the two models. These differences are substantial and because CP is partitioned very differently they have a significant impact on diet formulation as well as evaluation. They also present very different pictures of what to expect from this diet with respect to animal performance and the state of the animal’s body reserves of energy and protein.

21 CNCPS 6.1 and NRC 2001 Predictions of MP From MCP Yield CNCPSNRCDiff%NRC MP from Rumen Microbial sources*1340g1328g12g100.9% *MCP Microbial Crude Protein Though the two predictions of MP from rumen microbial yield are very similar the predictive mechanisms are not. As illustrated by the substantial difference in predicted MP allowable milk (18.3 lbs), the CNCPS and NRC models are partitioning CP very differently. The microbial CP yield that produced the CNCPS MP prediction above was calculated with dietary RDP of 9.05% DM. NRC calculated RDP at a little over 11.0% DM. If NRC had calculated RDP at 9.05% DM the NRC predictive mechanism would have reduced it’s prediction of MP from MCP yield to just 1211 g substantially lower than CNCPS at the same RDP%. In this diet, NRC would consider RDP at 9.05% DM to be limiting microbial yield. As stated above, though predicted MP from MCP yield is similar in this scenario, there still exists a significant potential that in other scenarios the two predictive mechanisms could move their predictions of MP from MCP yield substantially in opposite directions.

22 “Western Diet” Reformulated for AA Balance with CPM This table compares the CPM and NRC evaluations of the Western diet revised with CPM for balanced AA. As with the basal Western diet, there are also significant differences in model predicted energy and MP supplies here. Note that the difference in MP from microbial yield has increased from 256g in the basal diet to 290g. This difference in predicted MP from microbial yield constitutes 82.6% of the total difference between model evaluated MP supplies. Though there is also a difference in predicted MP from the diet, it is dwarfed by the difference in MP from microbial yield. The true significance of the disparity between model predictions of MP from microbial yield can best be seen when reviewing the predicted differences in supplies of MP-Lys and MP-Met. In that context, the percentage of difference attributable to the higher CPM prediction of MP from microbial yield is more than 90%.

23 “Western Diet” Reformulated for AA Balance with CPM On the MP-AA level, the impact of CPM predicted MP from microbial yield 290g greater than the NRC prediction is significant and accounts for 23.8g or all but 2.4g of the 26.2g difference in model predicted MP-Lys supplies and virtually all of the MP-Met difference. The model evaluated MP-Lys and MP-Met supply differences shown in the table above represent the difference between achieving the desired animal response and missing it altogether. It should be noted that the 24g of MP-Lys predicted from microbial yield has been formulated “out” of the diet because of the additional 290g of MP from microbial yield CPM predicts. If the additional microbial yield on which the 290g of MP and the additional 24g of MP-Lys are based isn’t present, the predicted animal response will not be realized and anyone is likely to conclude that balancing for AA “doesn’t work”. Having reviewed these important differences in the predictive mechanisms of CPM, CNCPS 6.1 and NRC let’s take a look at how to approach balancing for AA with Formulate2 and the NRC 2001 model.

24 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 With good sampling technique, accurate forage analysis that provides ALL of the inputs required by the NRC 2001 model, accurate forage characterization and good nutrient values for concentrates, our experience has been that the NRC model exhibits a very high degree of predictive reliability. Frequently, based on what is first limiting – typically MP allowable milk, NRC evaluations of diets have been within one pound of actual 3.5% FCM production. Consequently, we consider it to be a very reliable diagnostic tool as well as an excellent formulation model. With those thoughts in mind, the following structured approach to balancing for AA is presented. 1.In order to minimize response variability potential (RVP), estimates of MCP yield MUST accurately reflect what is experienced by the feeding group 2.Evaluate the existing diet with Formulate2/NRC 2001 on the basis of 3.5% FCM 3.Work up an accurate evaluation of the actual 3.5% FCM production and actual DMI of the feeding group receiving the diet 4.Use Formulate2 to scale formulated DMI to actual DMI and determine if actual FCM production is in line with predicted FCM (usually MP allowable milk will be first limiting) 5.If predicted FCM and actual FCM are not inline with each other, then determine what diet implementation or diet composition issues need to be resolved and address them 6.If all implementation and composition issues have been resolved, a significant difference still exists and MP allowable milk is first limiting, adjust model predicted MCP yield to accommodate the difference. 7.When performance and prediction are in line estimates of MCP yield are reliable 8.Re-balance the diet for AA content

25 Formulate2 Dairy Ration Optimizer An Overview Fully implements the NRC 2001 model and is 100% NRC compliant NRC 2001 prediction equations fully integrated with solution processes Provides full optimization capabilities within the NRC 2001 model framework What you constrain is what you get Integrated Lysine/Methionine and MP calculator based on recent research (Schwab et al.) to extend “…the application of NRC (2001) to predict lactation responses from changes in supply of MP-Lys and MP-Met.” (Amino Acid Balancing in the Context of MP and RUP Requirements, Schwab, Ordway and Whitehouse)

26 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Evaluate the existing diet with Formulate2/NRC 2001 on the basis of 3.5% FCM Work up an accurate evaluation of the actual 3.5% FCM production and actual DMI of the feeding group receiving the diet This is an excerpt from an assessment that was conducted in May of 2007 on behalf of a commercial dairy producer in Tulare county, California. The assessment was made based on the available data carefully collected and maintained by the producer along with FCM from the testers summary. These items from the Assessment Summary explain the method used to assess the diet then in place and illustrate the predictive reliability of the NRC model. All forages were sampled to provide accurate NRC forage input values and the method used to correct test day milk production data allowed reasonable estimates of DMI in each pen that correlated well with the producers record of overall herd DMI for this period. At the time of the assessment the herd was being fed a single TMR which helped to simplify the assessment process. Producer reported herd DMI was within a half pound of the formulated DMI estimate and when the formulated DMI was scaled to match the actual DMI, MP allowable milk moved from lbs to – within one half pound of actual milk flow at actual DMI.

27 RDP Balance of Consumed Diets as Predicted by NRC (2001) (UNH Boucher et al.) NRC Predictive Reliability This work was done at UNH by Boucher et al. and serves to illustrate the reliability of the NRC predictive mechanism for RDP requirements and microbial yield. Dietary RDP was manipulated with the addition of Urea at four different levels of from 0.0%DM to 0.9% DM. RDP levels in the graph to the right are expressed as a percentage of the NRC predicted RDP requirement ranging from 92% of the NRC requirement to 117% of requirement. Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009

28 Average Rumen Ammonia N Concentrations (UNH Boucher et al.) quadratic, P < 0.05 NRC Predictive Reliability This graph illustrates the measured Rumen Ammonia N concentrations at the different percentages of NRC predicted RDP requirement. Note that when RDP balance exceeded 109% of the NRC requirement, Ammonia N concentrations spiked significantly indicating that RDP much above the NRC requirement did not produce increased microbial yield. Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009

29 Flow of Microbial N to the Duodenum (UNH Boucher et al.) NRC Predictive Reliability Measured flows of Microbial N to the duodenum confirm the efficacy of the NRC predictive mechanism for both RDP requirements and microbial yield. Measured MCP yield was greatest when RDP was closest to 100% of the NRC predicted RDP requirement. quadratic, P < 0.05 Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009

30 Comparison of Measured Flow of Microbial N to the Duodenum with CPM and NRC 2001 Predicted Flows The data on Microbial N flow to the duodenum presented in the three previous slides prepared by Dr. Schwab summarizes work reported in the paper “Effect of Incremental Urea Supplementation of a Conventional Corn Silage Diet on Ruminal Ammonia Concentration and Synthesis of Microbial Protein” Boucher et al. UNH Journal of Dairy Science This study gives us an opportunity to compare CPM and NRC predictive mechanisms with measured flows of Microbial N to the duodenum. As can be readily seen in the table below, the NRC predictions of BacterialCP and MPBact are much closer to measured values than are the CPM predictions. Given that predictions of MP from microbial yield dictate the level of MP required from feeds when formulating diets, the differences illustrated below can significantly impact diet formulation and animal response. * Measured BacterialCP was calculated from reported Microbial N flow to the duodenum (243g * 6.25 = g CP). Measured MPBact was calculated from measured microbial N flow using the NRC efficiency factor for MP from MCP (1518.8g *.64 = 972.0g) ** Values in the NRC column were calculated using reported RDP% and the NRC equations for determining and constraining MCP yield when RDP balance is negative as reported in the study (20.7kg DMI * 9.2%RDP * 1000 = g RDP and *.85 = g MCP yield). The diet information presented in the paper was entered into CPM Dairy V Build 5 to obtain the CPM data shown above. The NRC data was calculated using the NRC prediction equations and the corresponding diet composition data presented in the study. CPMMeasured* BalanceNRC**Measured*Balance BacterialCP (g) MPBact (g) Predictive Efficacy – MP from Microbial CP in the Basal Diet

31 Differences in Prediction of MP from Microbial Yield As a further illustration of predictive differences, the CP% of the Alfalfa hay in the basal Western diet was reduced from 25.0% to 15.0%. This has the effect of significantly reducing dietary RDP (10.4% to 8.9%) while increasing dietary NFC (41.34% to 43.22%). With this adjustment in CP content the CPM predicted MP from microbial yield move from 1584g to 1612g. The effect of the same adjustment in NRC reduced the prediction of MP from microbial yield from 1328g to 1240g indicating that RDP balance was negative and therefore limiting microbial yield. In this scenario the two predictions are 372g apart. CPM NRC g spread

32 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Evaluate the existing diet with Formulate2/NRC 2001 on the basis of 3.5% FCM The NRC evaluation of the “Western Diet” tells us that if this animal is producing lbs of milk at this DMI she is doing so by mobilizing body reserves of fat and lean tissue to compensate for the dietary short-falls shown in this summary.

33 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Evaluate the existing diet with Formulate2/NRC 2001 on the basis of 3.5% FCM The Duodenal EAA Flow Summary of the “Western Diet” details supplies of MP-AA provided by the diet. Since our objective is to rebalance the diet for MP-Lys and MP-Met we’ll do so at the same DMI and compare our reformulated diet to the NRC evaluations of the basal diet including this EAA flow summary. We’ll also reformulate the diet to meet the same NE(l) allowable milk figure (92.11lbs) shown in the NRC Summary of NE(l) and MP Allowable Milk Production evaluation. Because we’ll be increasing concentrations of MP-Lys and MP- Met we’ll actually reduce the total dietary MP from RUP. This will produce a lower MP allowable milk value since the data used to determine NRC MP requirements was from diets where concentrations of MP-Lys and MP- Met were not optimized. The reduction in MP supply will represent an improved efficiency of use of MP resulting from increased concentrations of MP-Lys and MP-Met.

34 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content Using the Lys/Met and MP Calculator we’ll first determine the MP-Lys and MP-Met targets. The Calculator is an implementation of the work reported by Schwab et al. in the paper titled “Amino Acid Balancing in the Context of MP and RUP Requirements”. The calculator gives us the choice of selecting the values generated for either Lys or Met for milk or milk protein. For this illustration, the MP- Met value for milk TP has been selected. In addition, we can set the concentration of either MP-Lys or MP-Met in MP. This has the effect of reducing the total MP requirement based on the desired concentration in MP of the selected AA. The value shown as MP% Met was selected to provide a reduction in CP and also produce a reduction in cost from the basal diet. Once established, the target values for MP-Lys, MP-Met and MP are automatically exported to the diet record by clicking on the Export button. Because we’re significantly increasing the concentrations of Lys and Met in MP we’ll also make Wheat Millrun, Rice Bran, Almond Hulls, protected fat and sources of protected Lys and Met available to allow us to supply the targeted grams of MP-Lys and MP-Met with less total MP.

35 Plots of measured milk and protein yields vs. NRC (2001) predicted flows of MP–Met Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009 Lys:Met >3.0:1, MP more limiting than energy, and MP balance between –250 g and +100 g (n = 98) NRC Predictive Reliability The yield equations for milk and milk protein for MP- Met developed from these plots of measured data are implemented in the Calculator.

36 Plots of measured milk and protein yields vs. NRC (2001) predicted flows of MP–Lys. Lys:Met <3.2:1, MP more limiting than energy, and MP balance between –250 g and +100 g (n = 28) Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009 NRC Predictive Reliability The yield equations for milk and milk protein for MP- Lys developed from these plots of measured data are implemented in the Calculator.

37 Benefit of balancing for Lys and Met on increasing efficiency of use of MP 40 Kg milk obtained with 2800 g of MP (containing 5.7% Lys and 1.9% Met), 159 g MP-Lys and 53 g MP-Met. How much MP is needed to produce 40 Kg of milk if MP contains 6.6% Lys and 2.2% Met? 159 g MP-Lys / 6.6% Lys (.066) = 2409 g MP 53 g MP-Met / 2.2% Met (.022) = 2409 g MP How much MP is saved? 2800 g – 2409 g = 391 g How much RUP is saved? 391 g / 0.80 = 489 g Chuck Schwab University of New Hampshire, USA Foster Farms Dairy Nutrition Meeting Rio Casino & Hotel, Las Vegas, NV April 15, 2009 NRC Predictive Reliability This approach is implemented in the Calculator by specifying the concentration of the selected AA in MP which calculates the MP requirement based on the concentration in MP of the selected AA as illustrated here. This is all done on the basis of measured data from known results.

38 NRC Predictive Reliability Accounting for AA Profile Changes in RUP The NRC model was specifically designed to account for changes in the AA profile of RUP relative to intake CP in order to provide accurate prediction of flows of individual EAA to the duodenum. This was accomplished by comparing NRC model predicted supplies of AA in RUP with actual measured EAA in duodenal protein in 57 published studies with 199 diverse diets and developing prediction equations for each EAA based on those model predicted factors that best predicted the measured EAA flows. (Nutrient Requirements of Dairy Cattle, Seventh Revised Edition 2001 pg. 75)

39 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content Using the equations implemented in the Calculator we’ve set targets for both a specific number of grams of MP-Lys and MP-Met as well as their concentration in MP. Consequently, the resulting diet will at least meet these minimum values. To optimize the diet once the MP and AA target values are exported to the diet record select the “Solve Dynamic” option from the pop-up menu. Constraint values bound with an X in the bind column will be enforced at optimization time. Unbound values will be ignored. Note also that constraints for RDP as a percentage of DMI have also been set. This helps to insure that RDP supply will not limit microbial yield.

40 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content The NRC predicted DMI for this animal at this level of milk and milk component production is lbs or approximately 8.5% more than the “Western Diet”. In order to meet but not exceed the basal diet NE(l) allowable milk the diet will be reformulated at the predicted intake then scaled to the targeted DMI of 54.1 lbs. With this approach, the primary change will be the supplies of MP-Lys and MP-Met and their concentration in MP. Additional feed items available for inclusion will be pulled into the diet as their economic value warrants and as space is made available for them by the reduction in dietary RUP made possible as a result of the increased concentrations of MP-Lys and MP- Met in MP.

41 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content We’ll also make one other adjustment to our constraint scenario. Since we’re including MetaSmart which will provide not only MP-Met but also rumen available Met and we’ve made certain that RDP will not limit microbial yield and a workable minimum for NFC has been set, we’ll make a slight adjustment in the model predicted MCP yield. This adjustment is made based on Adisseo research showing that rumen available Met has a stimulatory effect on MCP yield. The adjustment will be applied during optimization and will have the effect of further reducing MP required from the diet.

42 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content This is the diet solution for the revised Western Diet with balanced MP-Lys and MP-Met. Note that the DMI of the diet has been scaled to 54.1 lbs. providing us with a direct comparison to the basal Western Diet. All of the additional feed items have been included in the solution and have provided economic as well as nutritional benefit. The total cost of the revised diet is $6.50 or $.04 less than the basal diet. However, the cost of concentrates dropped from $3.93 to $3.74. If forages are already in inventory and paid for this $.19 hd/day reduction in the cost of concentrates represents a significant improvement in monthly cash flow.

43 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content This summary of the constraints and supplies shows that concentrates moved from 53%DM in the basal diet to 50%DM in the revised diet. CP also dropped from 17.7%DM to 17.3%DM which still provides a buffer above the NRC RDP requirement. Note that the NRC model is dynamic with respect to energy and protein fractions where actual values are determined by level of DMI above maintenance and diet composition. Because we’ve scaled DMI to a lower level than that at which the diet was formulated, supplies of MP, RDP and Microbial yield will vary from the formulated values and may appear either above or below user constraints.

44 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content The AA values reported in the Duodenal EAA Flow Summary result from scaling DMI from 58.7 lbs to 54.1 lbs. The reformulated diet has increased supplies of both MP-Lys and MP-Met (MP-Lys +10g MP-Met +5.5g). Also, concentrations of MP- Lys and MP-Met have increased from 6.16%MP and 1.94%MP to 6.72%MP and 2.21%MP. Note also that MP-Lys level in the NRC reformulated diet is approximately 15g higher than the NRC evaluated MP-Lys in Dr. Sloan’s revised diet (164.87g vs g). These changes in supplies of MP-Lys and MP-Met are also predicted to increase yields of both milk and milk protein.

45 NRC Model Predictive Reliability Establishing a Structured Approach to Balancing for AA with NRC 2001 and Formulate2 Re-balance the diet for AA content The predicted result of changes in supplies of MP-Lys and MP-Met are shown in these two images of the Calculator. The Evaluate Lys/Met Supplies tab of the Calculator implements the Schwab et al. plot equations in a format that allows users to enter supply values for MP-Met and MP-Lys. It then calculates the predicted yields of Milk and TP. The TP percentages are merely notations of the percentages of predicted TP in the predicted milk volume and are not generated by the equations themselves. The predicted change in milk flow shown above is approximately lbs and the predicted change in TP is approximately + 75g. Original Western DietNRC Reformulated Diet

46 Diet Costs and RVP Factors What are the Risks? We now have a revised diet balanced for AA content using Formuate2 and NRC It is appropriate at this point to note some very significant differences between this diet and the revised diet formulated with CPM. The Western Diet reformulated by Dr. Sloan for AA balance with CPM has a significantly lower total cost than the NRC diet reformulated with Formulate2 – $6.38 compared to $6.50. However, there are risks associated with the $.12 difference in cost. Because of the risk involved in achieving the lower cost, it is crucial to understand the primary factor producing the cost difference. Perhaps the most direct method of illustrating both the factor producing the difference and its relationship to the risk is to note that it is possible to reduce cost of the reformulated NRC diet to the same cost of $6.38 To do so requires a simple, additional adjustment to the NRC predicted MCP yield. By moving the yield adjustment another 4 percentage points to 108 percent of the NRC prediction the cost of the diet is reduced to $6.38. However, the net effect of doing so removes 54g of MP from the diet in anticipation of 54g more MP from microbial yield – what happens if that additional MCP yield isn’t really there? Each percentage point above the NRC predicted MCP yield increases the risk of not achieving the predicted animal response. The same question applies to what is shown in the comparison of the two revised diets in this table. CPM is predicting 231g more MP from microbial yield than NRC – what happens if the MP isn’t really there because the microbial yield isn’t really there?

47 Diet Costs and RVP Factors What are the Risks? Moving the NRC predicted MP from MCP yield to 108% of the model prediction still leaves NRC predicted MP from MCP 177g short of the CPM prediction (1610g–1433g = 177g). Because the CPM predicted MP from MCP yield is so much higher, the risk with CPM is even greater. What would happen if the additional 231g of MP from MCP yield and the additional 127g of MP from feeds predicted by CPM above the NRC predictions shown in this table weren’t really there? Would balancing for AA with CPM “work”? Or, because so much MP is predicted from MCP yield and therefore formulated “out” of the diet, would achieving the predicted response be “inconsistent” and “unpredictable” at best? And what about CNCPS 6.1 with a prediction in the basal diet of MP from feeds more than 130% greater than NRC? Is it reasonable to assume that NRC is “that far off” when the NRC model has demonstrated a high level of predictive reliability with respect to MP allowable milk in evaluations of diets fed to dairy herds in commercial production settings? What has been examined in this presentation speaks volumes with respect to the efficacy of constraining theoretical mechanisms with actual measured data – particularly when formulating diets for AA balance in commercial production settings.


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